Neural Network Concept Tree
Explore how neural networks are constructed from layers, weights, activation functions, loss functions, and optimizers.
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About Neural Networks
Neural networks are computational models that learn to approximate complex functions through composition of simpler transformations. Each layer applies a linear transformation followed by a nonlinear activation, and the network is trained by optimizing a loss function using gradient-based methods. This concept tree breaks down the architecture and training process.